Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm

Zhe Sun, Ning Wang, Yunrui Bi and Dipti Srinivasan

Energy, 2015, vol. 90, issue P2, 1334-1341

Abstract: In this paper, a HADE (hybrid adaptive differential evolution) algorithm is proposed for the identification problem of PEMFC (proton exchange membrane fuel cell). Inspired by biological genetic strategy, a novel adaptive scaling factor and a dynamic crossover probability are presented to improve the adaptive and dynamic performance of differential evolution algorithm. Moreover, two kinds of neighborhood search operations based on the bee colony foraging mechanism are introduced for enhancing local search efficiency. Through testing the benchmark functions, the proposed algorithm exhibits better performance in convergent accuracy and speed. Finally, the HADE algorithm is applied to identify the nonlinear parameters of PEMFC stack model. Through experimental comparison with other identified methods, the PEMFC model based on the HADE algorithm shows better performance.

Keywords: Proton exchange membrane fuel cell (PEMFC); Parameter identification; Hybrid adaptive DE algorithm (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (32)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:90:y:2015:i:p2:p:1334-1341

DOI: 10.1016/j.energy.2015.06.081

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